GLM 5.2 or Grok 4.7 — Which Is Better?

These models have different strengths. GLM 5.2 from Z.ai scores 6.7 out of 10 on QueryWise and ranks 24. Grok 4.7 from xAI currently scores 8.3 out of 10 and sits in 12 place. By the current score, Grok 4.7 is ahead.

The gap to QueryWise's ranking leader also shows the overall scale: Claude Opus 5.5 holds first place with a score of 9.9. So Grok 4.7 looks like a strong option within this pair, but it still has room to grow before reaching the top of the ranking.

Answer quality: Grok leads on the overall score

In QueryWise's overall evaluation, Grok 4.7 outperforms GLM 5.2. That does not mean GLM performs worse at everything. It has a strong profile for specific tasks, while the final score combines different types of requests, including accuracy, reasoning, coding, and performance on long contexts.

GLM 5.2 scores 8.7 for coding and 9.2 for reasoning. Separate scores for Grok 4.7 in these two areas have not yet been published: its QueryWise card lists — for coding and — for reasoning. It would therefore be misleading to call Grok the winner in every individual category. On the combined result, there is one winner: Grok 4.7.

We tested GLM 5.2 with real questions that required it to keep several conditions in mind within a single answer. For example, when asked to “compare two versions of a lease agreement and highlight penalties, notice periods, and risks for the tenant,” it breaks the material down clearly and confidently. GLM is practical for this kind of work: rather than trying to impress, it quickly builds a useful structure.

Grok 4.7 is the more logical choice when you need a stronger overall answer rather than a narrow code or formal-logic check. A question such as “why might Kazakhstan's inflation indicator fall even though prices in stores continue to rise?” requires connecting several causes without confusing everyday inflation with official statistics. On these multi-layered explanations, Grok's higher overall score has real practical value.

Speed and pricing in tenge

According to QueryWise telemetry, GLM 5.2 responds in a median of 13 seconds. Measurement reliability is 100%. That is a strong result for users who ask many short questions in a row: responses do not take long to arrive, and a retry caused by a failure does not consume the budget.

There is not yet enough data on Grok 4.7's median speed and reliability. So when speed is the deciding factor, GLM currently has a demonstrated advantage rather than just a better-sounding promise. Grok may be faster in some sessions, but it is too early to confirm that with a number.

An average question costs about 10 ₸ with GLM 5.2 and about 10 ₸ with Grok 4.7. In everyday use, the difference is practically negligible: ten questions cost roughly one hundred tenge with either model. For a one-off request, price does not settle the argument; for regular work, answer quality and the number of follow-up corrections matter more.

Both models are available on QueryWise from Kazakhstan. Payments are processed in tenge, the interface is available in Russian and Kazakh, and new users get three free questions to start. On QueryWise, you can send the same prompt to GLM 5.2 and Grok 4.7 and immediately see where they agree and where they diverge.

Context and task types

GLM 5.2 works with a context of 1M, while Grok 4.7 has a window of 500K. In practice, that is the difference between handling a large archive of materials and working with a long document. If you need to analyze a 200-page contract, a set of internal policies, or a lengthy customer correspondence in one pass, GLM's extra capacity is more useful.

There is also a more down-to-earth scenario. Imagine a table showing yearly sales by region in Kazakhstan and the question: “Find the months when revenue rises while the number of orders falls, then suggest three possible explanations.” GLM 5.2 is well suited to this kind of analysis, especially when the context also includes notes explaining the columns.

Grok 4.7 makes more sense for complex general research, editing text with several constraints, or discussing a contentious idea. For example, if you need to prepare arguments for and against launching a new plan, separate verifiable facts from assumptions, and then reduce the result to a short memo for an executive, its higher overall score looks more convincing.

For coding, the choice is less clear-cut. GLM 5.2 already has a separate score of 8.7, and its 1M context is useful for a large project with multiple files. Grok 4.7 does not yet have a published — score, so based on confirmed data, GLM 5.2 is the coding winner. For a workflow that requires debugging several modules in one request, I would start with GLM.

Choose based on your use case

If you want the best overall result, choose Grok 4.7: its current QueryWise score is higher. For explaining a difficult subject, preparing coherent text, and handling multi-step reasoning, Grok 4.7 looks stronger within this pair.

If long documents, proven speed, stability, and coding matter most, GLM 5.2 is the more rational choice. It responds in 13 seconds, shows 100% reliability in our telemetry, and has a coding score of 8.7. The price is almost identical, so you do not have to pay extra for Grok—but you do not save money with it either.

My verdict is simple: Grok 4.7 wins the overall comparison, while GLM 5.2 leads on the confirmed technical metrics. For real work on QueryWise, it makes sense to start with three free questions for both models, then keep the one whose answers require fewer corrections in your particular workflow.